Comparative Study of Swarm Intelligence Behavior to Solve Optimization Problems

Abstract

The optimization problems usually need specific techniques to solve, therefore many approaches and methods were proposed to solved such problems, but there are many difficulties (limitations) still faced the problem solvers such as how to reach the solution (or solutions) with high performance and efficiency or with more accuracy results or with suitable behavior. Thus the artificial intelligence tools are considered the best tools that can be used to solve the optimization problems, because the AI tools must decide two importantaims: the problem reduction and the guarantee of solutions which lead to less the effect of the difficulties (limitations) and give more suitable criteria in performance, efficiency, and behavior. The swarm intelligent techniques are considered the most modern AI techniques which contains many approaches that are used to solve optimization problems with high performance and efficiency and suitable behaviorIn this paper a specific study is made to the behavior of the swarm intelligence techniques and evaluates its performance to solve various problems, then there is a presentation to a scientific comparative section in which many approaches is presented that used different swarm intelligence techniques such as Ant Colony Optimization (ACO), Bees Algorithm (BA), and Particle Swarm Optimization (PSO) to solve various optimization problems and them make a comparison among them interm of behavior and performance. Finally we reach to scientific discussion and conclusions that distinguish among the presented approaches to prove that the swarm intelligence techniques success in solving practical, important, and applicable problems with high performance, efficiency, and special behavior.